Jul

22

Are you an analytics beagle or a performance wolf?

Filed Under Practicing Web Analytics | Leave a Comment

So, I can’t even begin to describe how lame I feel this analogy is, but it’s effective, I think.

Beagles are great hunters.  They have an incredible nose, they’re very smart, they have great endurance and they’re faster than you’d ever imagine.

Wolves are also great hunters.  Fast, silent, great instincts, and accurate.

So what’s the difference between the two?  While one is pointing and barking at the issue at hand, hoping that someone else will finish the job, the wolf is dining.

What I see these days is a lot of beagles.  Incredibly smart, diligent, and creative people who bark and point at problems, letting the company know, “Over here guys!  Come take a look!”  The beagles will hang out comfortably by the fire until they’re called to duty, asked by the master to find what the company is hunting for.  They’re given a scent and they’re off on the trail. The company pours some puppy chow into their bank account every few weeks and pats them on the head as they progress in skill.  If they do a great job, they might get a nicer mat to sleep on or a shiny new collar.  But someone else is called in to handle the problem, and those people end up getting ahead.

The wolf, however, actively hunts.  They don’t wait for a master to ask them to eat, they feel the hunger and get off their wolf asses and kill something.  They drag the kill back to the den and feed their young, training them how to kill for themselves, be independent, defend themselves.  They also choose and plan their approach, starting with nearby meals that will be easier to bring down.  When they need to bring down a large beast, they collaborate seamlessly, knowing there will be plenty of time to argue over who eats first after the beast is tackled.

So, if you’re an analytics practitioner, what do you do? If you only know how to follow a scent, learn how to kill.  Learn design.  Learn usability.  Learn HTML, PHP, SQL, etc. Learn the financial backbone of the business – the core drivers of success.  Stop talking about page views and start talking about profit.  Actively seek problems that you know in your gut and bring them down with the data you know how to retrieve better than anyone.  Stop settling for being told what to look for, where to go.  Start getting hungry, and take all of the credit that’s due to you, sharing the credit that’s due others.  Teach others how to kill.  Be a wolf.

If you’re a company struggling with crap analysts, make some good hires, pay for training, and reward performance.  Not report-producing performance, PROFIT-enhancing performance.  Dis-reward (I know that’s not a word, thank you very much) the soft performance measures of old.  The kid staying until 9:00 pm to produce a report that shows you 5 data points and 0 recommendations is on the chopping block.  The kid staying until 4:00 and taking a 2-hour lunch who gives you 1 data point to support 10 recommendations is going to the corner office, and she desperately needs a better manager who can challenge her and set the bar.

Are you a beagle or a wolf today?  Don’t be a beagle tomorrow.

Jul

16

3.5 things that keep you from finding good web analytics people

Filed Under Web Analytics in Business | Leave a Comment

Probably the most frequently asked question I get is, “Evan, how can I tell a good analyst from a bad one?  What should I be looking for in a resume?”

Of course, the astute analyst will immediately recognize that those are two questions.  Well done!

Usually, my answer is a simple one: “You probably already have everything you need; you just need to start asking more / better questions of the people you have.”  The truth is that most businesses have already done a pretty decent job of hiring, believe it or not.  People who know me well will probably be surprised to hear me say that, but it’s true.

So what’s the problem?  Well, cue the first the three “things”:

Thing 1: Good web analytics people are carefully disguised as . . . your own employees!

What?  Yes, it’s true.  Good web analytics practitioners are all over the place.  They’re designers.  They’re usability people.  They’re your product managers, and even sometimes your IT people.  What these people don’t have, though, is your trust, the right tools and training, and an environment where they can collaborate and learn.

It’s very important that a large organization acknowledges the value of a healthy cat fight, but demands that each fighter does their homework.  Most everyone has an opinion on how things should be done, and a lot of those opinions are valid, but nobody is going to concede or collaborate if there isn’t some degree of data involved, and that’s where analytics comes in.  See, these people are yearning to be part-time analysts, and some of them full-time, if it wasn’t seen as a reporting job.

So what do you do? First, when people say, “I’d like to learn more about . . .,” make sure your gut reaction is not asking them why they want to learn something new, or even worse, why they don’t know this stuff already.  They’re asking you how to be a better employee, so don’t be a jerk – help them help you, Jerry Maguire style.

Thing 2: People with web analytics “experience” are most often not your most promising web analytics candidates.

This is the part where you start wondering if there is any good news.  It’s coming, I promise.  But first, let’s keep chipping away at reality.

The sad truth is this: very, very few businesses have figured out how to take advantage of web analytics in their organization.  They have competitive intelligence people, business intelligence people, product strategists, upper management, designers, and more, all telling the business, “Here’s what we need to do differently.”  Add to that the fact that most of these people are using different tools to measure and justify what they’re dreaming up, so they all get different numbers for the same thing, which makes the executives not trust any of it, or side with some numbers and not with others, based on which vendor paid for the more expensive steak dinner.

Yes, we know it’s silly, but it’s true.  No amount of us analysts saying, “That’s just silly logic,” is going to change this behavior, so it’s incumbent on us to figure out another, more successful way of convincing management that even though the numbers aren’t the “right”, they’re all “correct.”

The result of this organizational difficulty is that most people who have been hired as analysts are almost exclusively a source of reporting for a business.  While the analysts may add some text and reasoning to the report that they’re delivering, that only makes it a report with text, not an analytics deliverable.  These analysts, although they may indeed be talented, have been so stymied by their previous organization that they are too pacifist to argue their valid points and force your company to accommodate their valuable input.

You need fighters, and most current analysts have been too badly beaten: they’re scared dogs.

Thing 3: Your interview process prevents you from hiring good people.

Looking under the category of “hard to change,” we’ll see this one at the top of the list.  Here’s the deal: the people who are going to be interviewing  for the best web analytics person out there are going to be threatened as hell when they actually find one.  Let’s take Avinash Kaushik, for example.  He might be the nicest guy on the planet, but people know that if they hire him, things are going to change and that scares the hell out of them.

Things like peoples’ job performance, accuracy of insights, flaws in designs, poor calls to action, poor product planning and / or research will be revealed.  Gaps in knowledge will be exposed where people should be experts.  Flawed management techniques and approaches toward resolving conflict will be called to the mattresses.  Those who have decided to go with anecdotal choice A over anecdotal choice B will be asked why they didn’t consider data-driven choice C.

It’s important that the people doing the interviewing and hiring are able to get their own fears and egos out of the way.  I think that most people can easily identify the people who are intimidated or defensive, so just make sure that you involve some good people who are more interested in company success than personal protection, especially if they are junior to the true decision makers – they will quickly identify people they can learn from and want them nearby.

Thing 3.5: Your idea of an appropriate salary is way out of whack.

What fraction of a percent does your head web analytics guy have to move the conversion rate to pay for his whole salary in two weeks?  If you were to really Moses this one and part the seas for a talented person to point out flaws and implement recommendations, what would a 0.5% increase in conversion rate mean to your business – or even a 0.3% or o.1%?  If you’re a major (or even a middle-tier) online retailer, would this not equal hundreds of thousands of dollars of additional revenue per month?

So just think about it – if you want to put someone behind the steering wheel of your company’s data-driven decision making, how much should you be paying that person?  If you can get past what “analyst” means in the web space and start thinking of what “analyst” means in the financial space, for example, consider the potential impact that someone can have when they are highly effective at pointing out barriers to conversion AND architecting specific, implementable solutions.  I can tell you, it’s enormous.

Jul

9

Three enormous wastes of your web analytics time

Filed Under Practicing Web Analytics, Web Analytics in Business | 1 Comment

We are all guilty of wasting time, energy, and money. But when it comes to how organizations spend these three things on web analytics, or more appropriately mis-spend these resources, it can literally cost millions of wasted dollars when you consider how many people and days (or months) of work and senseless arguments it can drive.

There are a handful of things that we should stop doing immediately.  I’ve identified three here, most of which I’ll cover in greater detail in later posts.  I believe that if companies can reduce the amount of time they spend on these activities and increase the time they spend on practicing analytics, the world will be a significantly better place, your paychecks will increase, and you will want to hit inanimate objects less often.

  1. You are implementing analytics on new pages and tools last. I don’t know if I can tell you one example of web analytics implementation being a pervasive theme in content development.  Often, the product development people come up with an idea of what their content is going to do, they will come up with a handful of success metrics, and then once everything is built, analytics will be installed, usually requiring much of the more complex javascript – or even worse: flash – to be dragged back into the developer’s hands to open the hood and make sure this wonky tool can measure everything.

    It’s completely unacceptable in 2009 that a developer wouldn’t know the language of web analytics tools, the functions available for tracking in different ways, and your analysts (who will be the ones looking at all of this) not being a part of relevant conversations and directly involved in (and capable of) the physical implementation work.

  2. You care one iota about Unique Visitors and try to get the tool to count them correctly. Now this is certainly a topic I’ll want to cover in more detail in a future post, but let’s examine the facts:

    We know that the metric is ALWAYS wrong, across ALL tools    .  Period.  There is no fixing this metric when people delete cookies, change computers or browsers, or have more than one person in a household.

    We know that we’re not going to act on the data    , other than to tell our advertisers, partners, or someone else who asks for the metric.  And you don’t need a web analytics person for that – just tell a secretary to look up the number and be done with it. If you’re not sure of the accuracy, see above.

    None of your marketing or referral traffic efforts can be controlled to target or not target unique visitors    .  With the exception of retargeting campaigns, which can also fail if cookies are deleted of different browsers are used, Uniques is not a metric that you can optimize to, nor is it a metric that you pay against.  You are paying and optimizing to impressions, clicks, visits, or some other de-personalized metric that can’t tell the difference.

    The biggest advertisers in the world have realized that it’s not about uniques    , it’s about “touches.”  Finally, the beauty of it is that advertisers like Coke and Pepsi already realize that they’ll succeed by talking to the same people over and over again, because that’s exactly what it takes.  And nowhere else in the world are people less loyal to your brand than on the web: search has made sure of that.  If I’ve been to REI a thousand times before, it doesn’t mean I’m not going to search for my next parka or pair of hiking boots on Google to see what’s out there.  With zero competitive barriers present, don’t be foolish enough to think that you have any loyal customers who don’t need a constant reminder of your presence and offering.
  3. You are trying to get the numbers from any 2 of your 10 tools to match, be close, resemble each other, anything!!!So, I feel your pain on this one but that’s where it ends.  Let’s face it: we’re all spoiled online and we think that just because our analytics tool can give us an inch, we should be looking for a mile.  It’s just not the case.The truth is probably a few-fold: your implementation sucks (it does, trust me), many of the metrics used in each report are different in some way, and you’re completely mis-using the tools to begin with.

    A good friend of mine used to work for WebTrends and came up with a simple, but brilliant statement.  ”We’re looking at web trends, not web accounting.”  Another way of looking at it is this way: if you were in the middle of the desert and had to choose between a pedometer and a compass, which one would you choose?

    Of course, the answer is obvious.  You’d much rather know if you’re going in the right direction than how many steps you’ve taken, and that’s exactly what web analytics tools offer us: insight into where we are going, if we’re going the right direction, how we should change course, etc.  But instead of listening to that information, we’re off firing people because we have two numbers that are unexplainably 8% off.

Let me know what the barriers to stopping with these modes of business are in your workplace.  What’s keeping us locked on the pedometer when the compass is what we asked for in the first place?